Literature DB >> 29799346

Predictors of postoperative motor function in rolandic meningiomas.

Malte Ottenhausen1, Kavelin Rumalla1, Iyan Younus1, Shlomo Minkowitz2, Apostolos John Tsiouris2, Theodore H Schwartz1,3.   

Abstract

OBJECTIVEResection of supratentorial meningiomas is generally considered a low-risk procedure, but tumors involving the rolandic cortex present a unique challenge. The rate of motor function deterioration associated with resecting such tumors is not well described in the literature. Thus, the authors sought to report the rates and predictors of postoperative motor deficit following the resection of rolandic meningiomas to assist with patient counseling and surgical decision-making.METHODSAn institution's pathology database was screened for meningiomas removed between 2000 and 2017, and patients with neuroradiological evidence of rolandic involvement were identified. Parameters screened as potential predictors included patient age, sex, preoperative motor severity, tumor location, tumor origin (falx vs convexity), histological grade, FLAIR signal (T2-weighted MRI), venous involvement (T1-weighted MRI with contrast), intratumoral hemorrhage, embolization, and degree of resection (Simpson grade). Variables of interest included preoperative weakness and postoperative motor decline (novel or worsened permanent deficit). The SPSS univariate and bivariate analysis functions were used, and statistical significance was determined with alpha < 0.05.RESULTSIn 89 patients who had undergone resection of convexity (80.9%) or parasagittal (19.1%) rolandic meningiomas, a postoperative motor decline occurred in 24.7%. Of 53 patients (59.6%) with preoperative motor deficits, 60.3% improved, 13.2% were unchanged, and 26.4% worsened following surgery. Among the 36 patients without preoperative deficits, 22.2% developed new weakness. Predictors of preoperative motor deficit included tumor size (41.6 vs 33.2 cm3, p = 0.040) and presence of FLAIR signal (69.8% vs 50.0%, p = 0.046). Predictors of postoperative motor decline were preoperative motor deficit (47.2% vs 22.2%, p = 0.017), minor (compared with severe) preoperative weakness (25.6% vs 21.4%, p < 0.001), and preoperative embolization (54.5% vs 20.5%, p = 0.014). Factors that trended toward significance included parafalcine tumor origin (41.2% vs 20.8% convexity, p = 0.08), significant venous involvement (44.4% vs 23.5% none, p = 0.09), and Simpson grade II+ (34.2% vs 17.6% grade I, p = 0.07).CONCLUSIONSResection of rolandic area meningiomas carries a high rate of postoperative morbidity and deserves special preoperative planning. Large tumor size, peritumoral edema, preoperative embolization, parafalcine origin, and venous involvement may further increase the risk. Alternative surgical strategies, such as aggressive internal debulking, may prevent motor decline in a subset of high-risk patients.

Entities:  

Keywords:  ADC = apparent diffusion coefficient; GTR = gross-total resection; imaging; meningioma; motor cortex; oncology; rolandic; surgery

Year:  2018        PMID: 29799346     DOI: 10.3171/2017.12.JNS172423

Source DB:  PubMed          Journal:  J Neurosurg        ISSN: 0022-3085            Impact factor:   5.115


  5 in total

1.  Histopathological Investigation of Meningioma Capsule with Respect to Tumor Cell Invasion.

Authors:  Takashi Sugawara; Daisuke Kobayashi; Taketoshi Maehara
Journal:  Neurol Med Chir (Tokyo)       Date:  2022-08-10       Impact factor: 2.036

2.  Intraoperative MRI for the microsurgical resection of meningiomas close to eloquent areas or dural sinuses: patient series.

Authors:  Constantin Tuleasca; Rabih Aboukais; Quentin Vannod-Michel; Xavier Leclerc; Nicolas Reyns; Jean-Paul Lejeune
Journal:  J Neurosurg Case Lessons       Date:  2021-02-22

3.  Evaluation of Magnetic Resonance Imaging for Microsurgical Efficacy and Relapse of Rolandic Meningioma.

Authors:  Peng Cao; Nianhua Wang
Journal:  Comput Intell Neurosci       Date:  2022-06-06

4.  Postoperative Long-Term Independence Among the Elderly With Meningiomas: Function Evolution, Determinant Identification, and Prediction Model Development.

Authors:  Haoyi Li; Huawei Huang; Xiaokang Zhang; Yonggang Wang; Xiaohui Ren; Yong Cui; Dali Sui; Song Lin; Zhongli Jiang; Guobin Zhang
Journal:  Front Oncol       Date:  2021-03-05       Impact factor: 6.244

5.  Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features.

Authors:  Hsun-Ping Hsieh; Ding-You Wu; Kuo-Chuan Hung; Sher-Wei Lim; Tai-Yuan Chen; Yang Fan-Chiang; Ching-Chung Ko
Journal:  J Pers Med       Date:  2022-03-24
  5 in total

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